> ## Documentation Index > Fetch the complete documentation index at: https://docs.inworld.ai/llms.txt > Use this file to discover all available pages before exploring further. # Experiments > Run, configure, and monitor A/B experiments end to end ## Overview The **Inworld's Unreal Runtime** lets you iterate on prompts, models, custom nodes/edges parameters, and configs (LLM and TTS) without redeploying code. This guide covers the full workflow: graph creation, Portal setup, and experiment rollout. ## Experiments workflow summary Design your graph and enable remote config. Upload JSON configs for the baseline graph and its variants. Define targeting rules and rollout percentages in the Portal. Track metrics, then promote the winning variant. ## Step 1 - Create a Graph asset ### 1. Code your solution In this example we developed a simple Blueprint that instantiates the graph, runs it, and prints the response on screen. ### 2. Design your graph The sample graph makes an LLM call:

It includes a custom node that appends "PREFIX" (when its `AddPrefix` boolean is enabled) and inputs A and B as: ``` PREFIX --- {A} --- {B} ```

### 3. Enable remote config Enable remote config inside the graph editor:

Graph executions return results as: * **AddPrefix is false**: `{Request text} --- {LLM response}` * **AddPrefix is true**: `PREFIX --- {Request text} --- {LLM response}` The baseline uses `Ministral-8b-Latest` with `AddPrefix = false`:

## Step 2 - Register variants Registering a variant tells Portal which configuration can participate in Experiments. Start with the baseline, then add variants. ### 1. Register the baseline variant * **Register Graph**:

* **Copy the Graph ID** from the Graph Editor:

* **Enter or paste the Graph ID** to the Portal:

### 2. Create baseline variant * **Export baseline JSON config** from the Graph Editor:

* **Click on created Graph** then **Create Variant** in the Portal:

* **Upload baseline JSON config** to the Portal:

### 3. Create GPT-5 variant * **Export GPT-5 config** from the Graph Editor:

* **Create and upload GPT-5 variant** to the Portal:

### 4. Create GPT-5 with AddPrefix variant * **Export GPT-5 config** from the Graph Editor:

* **Create and upload GPT-5 with prefix variant** to the Portal ## Step 3 - Start an experiment Open the **Targeting & Rollout** tab and set default variant:

Changing the default targeting updates the configuration for: * All currently running graph instances (on their next execution) * All newly compiled graphs Targeting changes may take time to propagate. ### Example results **Baseline execution**:

**Switch targeting to GPT-5**:

Next execution result:

**Switch targeting to GPT-5 with prefix**: Next execution result:

## Step 4 - Monitor & roll out Monitor your experiment results and deploy the winner: * **Watch metrics**: Monitor dashboards, traces, and logs while the experiment runs * **Gradual rollout**: Increase the winning variant's allocation gradually (50/50 → 70/30 → 90/10), then set it to 100% and retire old rules * **Rollback**: Roll back or tweak allocations if latency, errors, or business KPIs regress ## How Experiments work When a request hits your graph, the runtime decides whether to use the local configuration or a remote variant from Experiments: 1. Remote config must be enabled 2. The graph ID must be registered in Experiments and have at least one active rule that returns a variant ```mermaid theme={"system"} flowchart TD A[Graph Execution Request] --> B{Enable Remote Config == true} B -->|NO/DEFAULT| C[Static Config] C --> D[Use local graph configuration] B -->|YES| E[Remote Config] E --> F{Experiments returns a variant?} F -->|NO| G[Use local graph configuration] F -->|YES| H[Use Experiments variant] style A fill:#e1f5fe style D fill:#c8e6c9 style G fill:#c8e6c9 style H fill:#fff3e0 ``` **If remote config is enabled**, Experiments evaluates each request as follows: 1. **Local cache check**: If the compiled variant for this user is cached, it executes immediately; otherwise Experiments is queried 2. **Variant fetch**: Experiments evaluates your targeting rules, returns the selected variant, and falls back to the local configuration if no rule applies or the fetch fails 3. **Compile & cache**: The runtime compiles the variant payload, caches it, and executes the graph with the new configuration ## Troubleshooting ### Failed to resolve struct flag **Error**: `LogInworldRuntime: Failed to resolve struct flag: ....... with targeting key: ....... with error: Flag not found` If you see this error in the output log, it usually means one of the following: * Remote Config is enabled, but the graph was not registered in Portal * The Graph ID registered in Portal does not match the Graph ID in Unreal Engine ### Targeting changes not appearing **Issue**: I changed targeting and don't see changes on the next executions Sometimes targeting updates take time to propagate. Wait a few moments and retry—the new variant will be applied on the next graph execution once the change is fully propagated. ## Supported via Experiments ### Applied on next executions (no code redeploy) * Switch LLM/STT/TTS models or providers * Adjust node configuration (temperature, token limits, prompts) * Reorder/add/remove nodes while preserving the same inputs/outputs * Update processing logic (edges, preprocessing steps, data flow) * Modify custom node parameters * Modify custom edge conditions parameters ### Requires a code deployment * Deploying new custom nodes/edges * Changing processing functions for custom nodes/edges * Changing the graph's input/output interface